SoroushVahidi/scidocs huggingface dataset:
Rollout Sampling Policy Iteration for Decentralized POMDPs
(2010)

We present decentralized rollout sampling policy iteration (DecRSPI) — a new algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI is designed to improve scalability and tackle problems that lack an explicit model. The algorithm uses MonteCarlo methods to generate a sample of reachable belief states. Then it computes a joint policy for each belief state based on the rollout estimations. A new policy representation allows us to represent solutions compactly. The key benefits of the algorithm are its linear time complexity over the number of agents, its bounded memory usage and good solution quality. It can solve larger problems that are intractable for existing planning algorithms. Experimental results confirm the effectiveness and scalability of the approach.
Internet of Robotic Things-Converging Sensing / Actuating , Hypoconnectivity , Artificial Intelligence and IoT Platforms
(2017)

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need
New folded configuration of rectangular waveguide filters with asymmetrical transmission zeros
(2014)

This paper presents a novel filter topology for the implementation of asymmetric responses with transmission zeros (TZs) in rectangular waveguide technology. It is based on a compact folded E-plane arrangement where adjacent resonators are capacitively coupled through rectangular slots, and non-adjacent resonators are coupled through simple inductive windows. The new folded configuration allows the introduction of transmission zeros above the passband, which can be easily controlled. High order filters can be designed by cascading an arbitrary number of resonators in a folded layout. Components based on this novel configuration are amenable to simple manufacturing processes and can be used in high-power environments. A triplexer containing filters implemented with the proposed topology has been designed. Measurements of a manufactured prototype are included to validate the use of this topology in practical applications.
Microstrip series fed antenna array for millimeter wave automotive radar applications
(2012)

A 2D tapered microstrip antenna array is analyzed and designed in the paper by adopting the method of Modified Transmission Line Model. The pattern of the E-plane is determined by 20 linear series fed rectangular microstrip patches with tapered width, and 16 linear arrays compose the 2D array fed by four-stage T-junction power divider network. The excitation coefficients in both E and H plane follow the Taylor distribution. The antenna array achieves a pencil beam of 29dB gain at W band, and its half power beamwidths(HPBW) for E plane and H plane are both about 5°. The Side Lobe Level (SLL) of the pattern at the center frequency is lower than -17dB and 19dB at E and H plane respectively.
Calibration of a dual-laser triangulation system for assembly line completeness inspection
(2012)

In controlled industrial environments, laser triangulation is an effective technique for 3D reconstruction, which is increasingly being used for quality inspection and metrology. In this paper, we propose a method for calibrating a dual laser triangulation system - consisting of a camera, two line lasers, and a linear motion platform designed to perform completeness inspection tasks on an assembly line. Calibration of such a system involves the recovery of two sets of parameters - the plane parameters of the two line lasers, and the translational direction of the linear motion platform. First, we address these two calibration problems separately. While many solutions have been given for the former problem, the latter problem has been largely ignored. Next, we highlight an issue specific to the use of multiple lasers - that small errors in the calibration parameters can lead to misalignment between the reconstructed point clouds of the different lasers. We present two different procedures that can eliminate such misalignments by imposing constraints between the two sets of calibration parameters. Our calibration method requires only the use of planar checkerboard patterns, which can be produced easily and inexpensively.
Adiposity and cancer risk: new mechanistic insights from epidemiology
(2015)

Excess body adiposity, commonly expressed as body mass index (BMI), is a risk factor for many common adult cancers. Over the past decade, epidemiological data have shown that adiposity–cancer risk associations are specific for gender, site, geographical population, histological subtype and molecular phenotype. The biological mechanisms underpinning these associations are incompletely understood but need to take account of the specificities observed in epidemiology to better inform future prevention strategies.
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
(2013)

Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis. This paper presents a new analysis of Stochastic Dual Coordinate Ascent (SDCA) showing that this class of methods enjoy strong theoretical guarantees that are comparable or better than SGD. This analysis justifies the effectiveness of SDCA for practical applications.
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
(2016)

Many classical algorithms are found until several years later to outlive the confines in which they were conceived, and continue to be relevant in unforeseen settings. In this paper, we show that SVRG is one such method: originally designed for strongly convex objectives, is also very robust under non-strongly convex or sum-of-non-convex settings. If f(x) is a sum of smooth, convex functions but f is not strongly convex (such as Lasso or logistic regression), we propose a variant SVRG that makes a novel choice of growing epoch length on top of SVRG. SVRG is a direct, faster variant of SVRG in this setting. If f(x) is a sum of non-convex functions but f is strongly convex, we show that the convergence of SVRG linearly depends on the non-convexity parameter of the summands. This improves the best known result in this setting, and gives better running time for stochastic PCA.
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
(2013)

Stochastic gradient descent is popular for large scale optimization but has slow convergence asymptotically due to the inherent variance. To remedy this problem, we introduce an explicit variance reduction method for stochastic gradient descent which we call stochastic variance reduced gradient (SVRG). For smooth and strongly convex functions, we prove that this method enjoys the same fast convergence rate as those of stochastic dual coordinate ascent (SDCA) and Stochastic Average Gradient (SAG). However, our analysis is significantly simpler and more intuitive. Moreover, unlike SDCA or SAG, our method does not require the storage of gradients, and thus is more easily applicable to complex problems such as some structured prediction problems and neural network learning.
Accelerated Proximal Gradient Methods for Nonconvex Programming
(2015)

Nonconvex and nonsmooth problems have recently received considerable attention in signal/image processing, statistics and machine learning. However, solving the nonconvex and nonsmooth optimization problems remains a big challenge. Accelerated proximal gradient (APG) is an excellent method for convex programming. However, it is still unknown whether the usual APG can ensure the convergence to a critical point in nonconvex programming. In this paper, we extend APG for general nonconvex and nonsmooth programs by introducing a monitor that satisfies the sufficient descent property. Accordingly, we propose a monotone APG and a nonmonotone APG. The latter waives the requirement on monotonic reduction of the objective function and needs less computation in each iteration. To the best of our knowledge, we are the first to provide APG-type algorithms for general nonconvex and nonsmooth problems ensuring that every accumulation point is a critical point, and the convergence rates remain O ( 1 k2 ) when the problems are convex, in which k is the number of iterations. Numerical results testify to the advantage of our algorithms in speed.
Audio Signal Classification: History and Current Techniques
(2003)

Audio signal classification (ASC) consists of extracting relevant features from a sound, and of using these features to identify into which of a set of classes the sound is most likely to fit. The feature extraction and grouping algorithms used can be quite diverse depending on the classification domain of the application. This paper presents background necessary to understand the general research domain of ASC, including signal processing, spectral analysis, psychoacoustics and auditory scene analysis. Also presented are the basic elements of classification systems. Perceptual and physical features are discussed, as well as clustering algorithms and analysis duration. Neural nets and hidden Markov models are discussed as they relate to ASC. These techniques are presented with an overview of the current state of the ASC research literature.
Potassium Incorporation for Enhanced Performance and Stability of Fully Inorganic Cesium Lead Halide Perovskite Solar Cells.
(2017)

Thermally unstable nature of hybrid organic-inorganic perovskites has been a major obstacle to fabricating the long-term operational device. A cesium lead halide perovskite has been suggested as an alternative light absorber, due to its superb thermal stability. However, the phase instability and poor performance are hindering the further progress. Here, cesium lead halide perovskite solar cells with enhanced performance and stability are demonstrated via incorporating potassium cations. Based on Cs0.925K0.075PbI2Br, the planar-architecture device achieves a power conversion efficiency of 10.0%, which is a remarkable record in the field of inorganic perovskite solar cells. In addition, the device shows an extended operational lifetime against air. Our research will stimulate the development of cesium lead halide perovskite materials for next-generation photovoltaics.
Probabilistic Student Models: Bayesian Belief Networks and Knowledge Space Theory
(1992)

The applicability of Knowledge Space Theory (Falmagne and Doignon) and Bayesian Belief Networks (Pearl) as probabilistic student models imbedded in an Intelligent Tutoring System is examined. Student modeling issues such as knowledge representation, adaptive assessment, curriculum advancement, and student feedback are addressed. Several factors contribute to uncertainty in student modeling such as careless errors and lucky guesses, learning and forgetting, and unanticipated student response patterns. However, a probabilistic student model can represent uncertainty regarding the estimate of the student's knowledge and can be tested using empirical student data and established statistical techniques.
Sentence Segmentation in Narrative Transcripts from Neuropsycological Tests using Recurrent Convolutional Neural Networks
(2017)

Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of languageimpairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech, as well as, normal, prepared speech, and presents better results for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive Impairment (MCI) patients (F1 = 0.70) than the Conditional Random Fields method (F1 = 0.55 and 0.53, respectively) used in the same context of our study. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by MCI and CTL.
Firefly: illuminating future network-on-chip with nanophotonics
(2009)

Future many-core processors will require high-performance yet energy-efficient on-chip networks to provide a communication substrate for the increasing number of cores. Recent advances in silicon nanophotonics create new opportunities for on-chip networks. To efficiently exploit the benefits of nanophotonics, we propose Firefly - a hybrid, hierarchical network architecture. Firefly consists of clusters of nodes that are connected using conventional, electrical signaling while the inter-cluster communication is done using nanophotonics - exploiting the benefits of electrical signaling for short, local communication while nanophotonics is used only for global communication to realize an efficient on-chip network. Crossbar architecture is used for inter-cluster communication. However, to avoid global arbitration, the crossbar is partitioned into multiple, logical crossbars and their arbitration is localized. Our evaluations show that Firefly improves the performance by up to 57% compared to an all-electrical concentrated mesh (CMESH) topology on adversarial traffic patterns and up to 54% compared to an all-optical crossbar (OP XBAR) on traffic patterns with locality. If the energy-delay-product is compared, Firefly improves the efficiency of the on-chip network by up to 51% and 38% compared to CMESH and OP XBAR, respectively.
Cross-domain recommendations without overlapping data: myth or reality?
(2014)

Cross-domain recommender systems adopt different techniques to transfer learning from source domain to target domain in order to alleviate the sparsity problem and improve accuracy of recommendations. Traditional techniques require the two domains to be linked by shared characteristics associated to either users or items. In collaborative filtering (CF) this happens when the two domains have overlapping users or item (at least partially). Recently, Li et al. [7] introduced codebook transfer (CBT), a cross-domain CF technique based on co-clustering, and presented experimental results showing that CBT is able to transfer knowledge between non-overlapping domains. In this paper, we disprove these results and show that CBT does not transfer knowledge when source and target domains do not overlap.
Shreds: Fine-Grained Execution Units with Private Memory
(2016)

Once attackers have injected code into a victim program's address space, or found a memory disclosure vulnerability, all sensitive data and code inside that address space are subject to thefts or manipulation. Unfortunately, this broad type of attack is hard to prevent, even if software developers wish to cooperate, mostly because the conventional memory protection only works at process level and previously proposed in-process memory isolation methods are not practical for wide adoption. We propose shreds, a set of OS-backed programming primitives that addresses developers' currently unmet needs for fine-grained, convenient, and efficient protection of sensitive memory content against in-process adversaries. A shred can be viewed as a flexibly defined segment of a thread execution (hence the name). Each shred is associated with a protected memory pool, which is accessible only to code running in the shred. Unlike previous works, shreds offer in-process private memory without relying on separate page tables, nested paging, or even modified hardware. Plus, shreds provide the essential data flow and control flow guarantees for running sensitive code. We have built the compiler toolchain and the OS module that together enable shreds on Linux. We demonstrated the usage of shreds and evaluated their performance using 5 non-trivial open source software, including OpenSSH and Lighttpd. The results show that shreds are fairly easy to use and incur low runtime overhead (4.67%).
Node Classification in Signed Social Networks
(2016)

Node classification in social networks has been proven to be useful in many real-world applications. The vast majority of existing algorithms focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks. It is evident from recent developments in signed social network analysis that negative links have added value over positive links. Therefore, the incorporation of negative links has the potential to benefit various analytical tasks. In this paper, we study the novel problem of node classification in signed social networks. We provide a principled way to mathematically model positive and negative links simultaneously and propose a novel framework NCSSN for node classification in signed social networks. Experimental results on real-world signed social network datasets demonstrate the effectiveness of the proposed framework NCSSN. Further experiments are conducted to gain a deeper understanding of the importance of negative links for NCSSN.
Dilemmas in a General Theory of Planning *
(2005)

The search for scientific bases for confronting problems of social policy is bound to fail, because of the nature of these problems. They are "wicked" problems, whereas science has developed to deal with "tame" problems. Policy problems cannot be definitively described. Moreover, in a pluralistic society there is nothing like the undisputable public good; there is no objective definition of equity; policies that respond to social problems cannot be meaningfully correct or false; and it makes no sense to talk about "optinaal solutions" to social probIems unless severe qualifications are imposed first. Even worse, there are no "solutions" in the sense of definitive and objective answers. George Bernard Shaw diagnosed the case several years ago; in more recent times popular protest may have already become a social movement . Shaw averred that "every profession is a conspiracy against the laity." The contemporary publics are responding as though they have made the same discovery. Few o f the modern professionals seem to be immune f rom the popular a t t a c k whether they be social workers, educators, housers, public health officials, policemen, city planners, highway engineers or physicians. Our restive clients have been telling us that they don ' t like the educational programs that schoolmen have been offering, the redevelopment projects urban renewal agencies have been proposing, the lawenforcement styles o f the police, the administrative behavior o f the welfare agencies, the locations o f the highways, and so on. In the courts, the streets, and the political campaigns, we've been hearing ever-louder public protests against the professions' diagnoses o f the clients' problems, against professionally designed governmental programs, against professionally certified standards for the public services. It does seem odd that this attack should be coming just when professionals in * This is a modification of a paper presented to the Panel on Policy Sciences, American Association for the Advancement of Science, Boston, December 1969.
Where Do Rewards Come From ?
(2009)

Reinforcement learning has achieved broad and successful application in cognitive science in part because of its general formulation of the adaptive control problem as the maximization of a scalar reward function. The computational reinforcement learning framework is motivated by correspondences to animal reward processes, but it leaves the source and nature of the rewards unspecified. This paper advances a general computational framework for reward that places it in an evolutionary context, formulating a notion of an optimal reward function given a fitness function and some distribution of environments. Novel results from computational experiments show how traditional notions of extrinsically and intrinsically motivated behaviors may emerge from such optimal reward functions. In the experiments these rewards are discovered through automated search rather than crafted by hand. The precise form of the optimal reward functions need not bear a direct relationship to the fitness function, but may nonetheless confer significant advantages over rewards based only on fitness.
Team-based learning: systematic research review.
(2011)

Team-based learning (TBL) is an active learning method developed to help students achieve course objectives while learning how to function in teams. Many faculty members have adopted TBL because it is a unique teaching method, but evidence about its effectiveness is unclear. Seventeen original studies on TBL are presented in this systematic review of research. The studies include descriptive, explanatory, and experimental research published from 2003 to 2011 in the nursing, medical, education, and business literature. Generally, students are satisfied with TBL and student engagement is higher in TBL classes. Evidence also exists that students in TBL classes score higher on examinations. However, further high-quality experimental studies are needed to confirm that TBL positively affects examination scores and other learning outcomes and to determine whether TBL produces students who have the ability to function well in groups.
Demographics, weather and online reviews: a study of restaurant recommendations
(2014)

Online recommendation sites are valuable information sources that people contribute to, and often use to choose restaurants. However, little is known about the dynamics behind participation in these online communities and how the recommendations in these communities are formed. In this work, we take a first look at online restaurant recommendation communities to study what endogenous (i.e., related to entities being reviewed) and exogenous factors influence people's participation in the communities, and to what extent. We analyze an online community corpus of 840K restaurants and their 1.1M associated reviews from 2002 to 2011, spread across every U.S. state. We construct models for number of reviews and ratings by community members, based on several dimensions of endogenous and exogenous factors. We find that while endogenous factors such as restaurant attributes (e.g., meal, price, service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education) and weather (e.g., temperature, rain, snow, season) also exert a significant effect on reviews. We find that many of the effects in online communities can be explained using offline theories from experimental psychology. Our study is the first to look at exogenous factors and how it related to online online restaurant reviews. It has implications for designing online recommendation sites, and in general, social media and online communities.